Calculate Empirical Number of False Positives

Calculate the mean number of false positive features from a permutation analysis performed during a stability selection run. This assumes a permutation set was generated during stability selection, (i.e. num_perms > 0).

Usage

calc_emp_fdr(x, thresh_seq, warn = TRUE)

Arguments

x

A stab_sel class object generated via stability_selection().

thresh_seq

numeric(n). A sequence in [0, 1] specifying the thresholds to evaluate.

warn

logical(1). Should warnings be triggered if mean of < 5 permutations is being returned?

Value

A named vector indicating the average number (counts) of false positive features selected at the various thresholds specified by thresh_seq.

See also

get_threshold_features()

Other empirical FDR: calc_emp_fdr_breaks(), plot_emp_fdr()

Author

Stu Field, Michael R. Mehan

Examples

withr::with_seed(101, {
  n_feat      <- 20
  n_samples   <- 100
  x           <- matrix(rnorm(n_feat * n_samples), n_samples, n_feat)
  colnames(x) <- paste0("feat", "_", head(letters, n_feat))
  y  <- sample(1:2, n_samples, replace = TRUE)
})
ss <- stability_selection(x, y, "l1-logistic", num_iter = 25,
                          num_perms = 25, r_seed = 101, parallel = TRUE)
#> ✓ Using kernel: 'l1-logistic' and 1 core (serial)
calc_emp_fdr(ss, seq(0.5, 0.9, 0.1))
#> thresh_0.5 thresh_0.6 thresh_0.7 thresh_0.8 thresh_0.9 
#>      20.00      20.00      19.56      15.20       4.80